Date Published: February 11, 2019
Publisher: John Wiley and Sons Inc.
Author(s): Gabriel Dansereau, Tina W. Wey, Véronique Legault, Marie A. Brunet, Joseph W. Kemnitz, Luigi Ferrucci, Alan A. Cohen.
Two major goals in the current biology of aging are to identify general mechanisms underlying the aging process and to explain species differences in aging. Recent research in humans suggests that one important driver of aging is dysregulation, the progressive loss of homeostasis in complex biological networks. Yet, there is a lack of comparative data for this hypothesis, and we do not know whether dysregulation is widely associated with aging or how well signals of homeostasis are conserved. To address this knowledge gap, we use unusually detailed longitudinal biomarker data from 10 species of nonhuman primates housed in research centers and data from two human populations to test the hypotheses that (a) greater dysregulation is associated with aging across primates and (b) physiological states characterizing homeostasis are conserved across primates to degrees associated with phylogenetic proximity. To evaluate dysregulation, we employed a multivariate distance measure, calculated from sets of biomarkers, that is associated with aging and mortality in human populations. Dysregulation scores positively correlated with age and risk of mortality in most nonhuman primates studied, and signals of homeostatic state were significantly conserved across species, declining with phylogenetic distance. Our study provides the first broad demonstration of physiological dysregulation associated with aging and mortality risk in multiple nonhuman primates. Our results also imply that emergent signals of homeostasis are evolutionarily conserved, although with notable variation among species, and suggest promising directions for future comparative studies on dysregulation and the aging process.
Two outstanding questions in the biology of aging are (a) What general biological framework can integrate the diversity of physiological mechanisms underlying aging (Cohen, 2017a)? and (b) How and why do aging and longevity vary across species (Cohen, 2017b; Jones et al., 2014)? Recent research in humans has emphasized that aging is a product of complex system dynamics, rather than the sum of isolated mechanisms, leading to increased interest in physiological networks and feedback among different systems (Cohen, Martin, Wingfield, McWilliams, & Dunne, 2012; Fried et al., 2009; Han et al., 2017; Hoffman et al., 2014; Kriete, Bosl, & Booker, 2010). Accordingly, some analyses have moved from examining single candidate biomarkers of aging in isolation to multivariate approaches that address interrelated system functioning and dynamics and more directly test hypotheses about emergent processes. One process proposed to be an important driver of aging is physiological dysregulation, the progressive loss of homeostasis in complex biological networks. In this scenario, the consequences of aging largely result from system‐level breakdown of regulation, rather than problems in single mechanisms, such as gene expression or oxidative stress (Cohen et al., 2012). A number of studies in humans now support this hypothesis, showing that dysregulation can increase with age and predict increased mortality or other health risks (Arbeev et al., 2016; Cohen et al., 2013; Crimmins, Johnston, Hayward, & Seeman, 2003; Fried et al., 2009; Glei, Goldman, Chuang, & Weinstein, 2007; Karlamangla, Singer, & Seeman, 2006; Yang & Kozloski, 2011).
Biomarker data came from long‐term human datasets and systematic longitudinal measures of nonexperimental NHPs in research centers. The 10 NHP species in this study spanned a range of taxa and expected maximum lifespans (Table 1). To measure DM, we strove to maximize the number and diversity of biomarkers to capture multiple systems, while maintaining sufficient sample sizes (Supporting information Table S1). The number of biomarkers used varied among species, ranging from 10 to 24. To check the effect of biomarker availability/choice, we replicated all analyses in two sets of data: Set 1 used variable biomarkers (10–24) depending on availability for 11 species (humans and 10 NHPs) and Set 2 used 12 fixed biomarkers (Supporting information Tables S1 & S2) across 10 species (humans and 9 NHPs). Set 2 excluded the species with the fewest available biomarkers. Thus, Set 1 provides a better representation of biomarkers, but the calculation of DM is more species‐specific. For models of aging and health risks, we calculated DM using the first (i.e., the youngest) observations for each individual of each species as the reference population for itself, while for cross‐species comparisons, we compared DM scores calculated from different possible reference populations.
Our key findings are twofold. First, physiological dysregulation tends to increase with age and/or predicts mortality risk across diverse primate species. This confirms a role for dysregulation in a broader phylogenetic context, although with substantial nuances and among‐species variation. Second, homeostatic signature is overall moderately conserved across primate species, declining with phylogenetic distance. This is striking given the population differences in homeostatic signature even within humans (Cohen et al., 2018, 2015), such that care is needed when using one population as a reference for another. Here, dysregulation scores calibrated within a focal species correlated well with scores obtained from pooling all species in a joint reference population, or, in some cases, even with scores calibrated on a different species. Moreover, the conserved pattern appears to be an emergent phenomenon of the system not directly predicted by its parts. This study provides the first broad evidence linking physiological dysregulation to aging and mortality across different species, and it illustrates the importance of using species comparisons to test the generality of the pattern.
G.D., T.W.W., and A.A.C. conceptualized and designed the initial study. G.D. designed and performed the first version of analysis. G.D. and T.W.W. analyzed the data. V.L. aided with analysis. G.D., T.W.W., A.A.C., V.L., J.W.K., and M.A.B. contributed to further development of study design and analysis. J.W.K. and L.F. provided expertise on the data used. T.W.W. wrote the first version and major revisions of the manuscript. All authors read and provided feedback on the manuscript.